F
licenseNot graded
qualityC
maintenanceEnables LLM clients to load, clean, visualize, and analyze tabular datasets, and to generate multi-format analytic reports through natural language instructions.
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MCP Data Science
by AstyanM
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TDQS
A3.5/5.0
Scored across 102 tools
Disambiguation3/5
Most tools have distinct purposes, but several families overlap: group_aggregate, group_aggregate_multi, and describe_by_group; variance_filter, correlation_filter, and drop_low_importance; and multiple encoding tools. With 102 tools, an agent has a real risk of selecting the wrong one despite helpful descriptions.
Naming Consistency4/5
The overwhelming majority follow a clear verb_noun snake_case pattern (load_csv, drop_duplicates, train_model, plot_histogram). A few outliers like crosstab, predict, and group_aggregate_multi break the pattern slightly, but the convention is highly predictable overall.
Tool Count1/5
102 tools is far beyond any reasonable scope for a coherent toolset, even for a broad data science domain. This dramatically increases cognitive load and selection difficulty, making the server feel more like a sprawling library than a focused MCP tool surface.
Completeness5/5
The toolset covers the entire data science lifecycle: loading, cleaning, EDA, visualization, feature engineering, encoding, modeling, evaluation, clustering, statistical testing, and reporting. Minor gaps exist (e.g., no model deletion or explicit model persistence), but there are no meaningful dead ends for the intended workflows.
Maintenance
ActivityInactive
ResponsivenessNo issues